ReproHack NL, 2019
ReproHack @ Leiden University
A hands-on Reproducibility Hackathon
📅 30th November, 2019
⏰ 10 am - 5pm
📍 Leiden University Library
Welcome 👋
What’s a ReproHack?
We are all excited by the progress made by many authors to make their papers reproducible by publishing associated code and data.
We know how challenging it can be so we want to showcase the value of the practice, both for original authors and as a learning experience for those who attempt to reproduce the work.
During a ReproHack, participants attempt to reproduce published research of their choice from a list of proposed papers with publicly available associated code and data. Participants get to work with other people’s material in a low pressure environment and record their experiences on a number of key aspects, including reproducibility, transparency and reusability of materials. At the end of the day we regroup, share our experiences and give feedback to the authors.
It’s imperative to note that ReproHacks are by no means an attempt to criticise or discredit work. We see reproduction as beneficial scientific activity in itself, with useful outcomes for authors and valuable learning experiences for the participants and the research community as a whole.
We strive to make this event open and inclusive to all. As such we ask you to read our Code of Conduct. By participating, you are expected to uphold this code.
Join the event and reproduce ♻️
Register and save your spot by clicking here!
Join us at the ReproHack and get working with other people’s material.
Benefits to participants:
Practical experience in reproducibility with real published materials and the opportunity to explore different tools and strategies.
Inspiration from working with other people’s code and data.
An appreciation that reproducibility is non trivial but that opening up your work for more people to engage with is the best way to help improve it. An appreciation that reproducibility has community value beyond just the validation of the results. For example, access to such materials increases the potential for reuse and understanding of the work.
Benefits to the whole research community:
Assessment of how reproducible papers are ‘out of the box’.
Evaluation of how successful current practices are and for what purpose.
Identification of what works and where the most pressing weaknesses in our approaches are.
Propose a paper 📜
Click here to nominate your paper!
You’ve put a lot of effort into making your work reproducible. Now let people learn from and engage with it!
We invite nominations for papers that have both associated code and data publicly available. We also encourage analyses based on open source tools as we cannot guarantee participants will have access to specialised licenced software.
Proposed papers:
1. Supercurrent-induced Majorana bound states in a planar geometry
NA
submitted by André Melo
Why should we attempt to reproduce this paper?
We’ve tried to make it as easy as possible to reproduce. There’s some fun physics on the paper and it’s all done with Python!
Paper URL: https://dx.doi.org/10.21468/SciPostPhys.7.3.039
Data URL: https://doi.org/10.5281/zenodo.2653483
Code URL: https://doi.org/10.5281/zenodo.2653483
Useful programming skills: Python
3. Impact of predictor measurement heterogeneity across settings on the performance of prediction models: A measurement error perspective
Luijken, K., Groenwold, R. H., Van Calster, B., Steyerberg, E. W., & van Smeden, M. (2019). Impact of predictor measurement heterogeneity across settings on the performance of prediction models: A measurement error perspective. Statistics in medicine.
submitted by Kim Luijken
Why should we attempt to reproduce this paper?
This methodological paper contains explanations and recommendations based on simulation studies. It is my first attempt at making a paper reproducible. I would be interested in knowing whether reproduced results are equal (or similar enough) and whether the code is user friendly.
Paper URL: https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8183
Data URL: https://github.com/KLuijken/Prediction_Measurement_Heterogeneity_Predictor
Code URL: https://github.com/KLuijken/Prediction_Measurement_Heterogeneity_Predictor
Useful programming skills: R
4. Improving on Adjusted R-Squared
Karch, J. (2019, September 16). Improving on Adjusted R-Squared. https://doi.org/10.31234/osf.io/v8dz5
submitted by Julian D. Karch
Why should we attempt to reproduce this paper?
First, I think I came quite far in making it reproducible. The paper uses a simulation study. All the steps from the raw results of the simulation study to the final manuscript can be reproduced by clicking one button. This works because all code + all dependencies are stored online within a virtual machine that anybody can access. So, I think it is a quite good example for others to learn from. Second, the code running the simulation study itself is not included in this process because it would take far too long to run on one machine. I ran the code on a supercomputer. I would be interested in how people would try to reproduce such long-running code and whether they have feedback on how to improve sharing such long-running code.
Paper URL: https://doi.org/10.31234/osf.io/v8dz5
Data URL: https://doi.org/10.24433/CO.8023088.v1
Code URL: https://doi.org/10.24433/CO.8023088.v1
Useful programming skills: R
5. Spatial modelling of rice yield losses in Tanzania due to bacterial leaf blight and leaf blast in a changing climate
Spatial modelling of rice yield losses in Tanzania due to bacterial leaf blight and leaf blast in a changing climate. C. Duku, A. H. Sparks, S. J. Zwart. Climatic Change 135.3-4 (2016) pp. 569–583. Springer Nature. doi: 10.1007/s10584-015-1580-2
submitted by Adam Sparks
Why should we attempt to reproduce this paper?
This was my third attempt at making a paper fully reproducible. To date I it’s the most reproducible that I have published. I’m interested to know what stumbling blocks exist that I’m not aware of (aside from needing software like ArcGIS to fully rerun the complete analysis).
Data URL: https://figshare.com/articles/MICORDEA/1408501
Code URL: https://github.com/adamhsparks/MICCORDEA
Useful programming skills: R, Python, ArcGIS
6. Climate change may have limited effect on global risk of potato late blight.
Sparks, A. H., Forbes, G. A, Hijmans, R. J., & Garrett K. A. (2014). Climate change may have limited effect on global risk of potato late blight. Global Change Biology, doi:10.1111/gcb.12587.
submitted by Adam Sparks
Why should we attempt to reproduce this paper?
This is a two-for one. The repository contains code for companion papers, the model development and the model implementation and analysis. As the repository notes, some data are not freely available so I’ve made an effort to allow the paper to be replicated as best possible with what’s available.
Paper URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/gcb.12587
Code URL: https://github.com/adamhsparks/Global-Late-Blight-MetaModelling
Useful programming skills: R
7. Sea level regulated tetrapod diversity dynamics through the Jurassic/Cretaceous interval
Tennant, J. P., Mannion, P. D., & Upchurch, P. (2016). Sea level regulated tetrapod diversity dynamics through the Jurassic/Cretaceous interval. Nature Communications, 7, 12737.
submitted by Jon Tennant
Why should we attempt to reproduce this paper?
Because it’s a fun paper, involving dinosaurs! But one which I myself have also attempted to reproduce in the past, and struggled with. There are a few additional tweaks that might throw some people off too.
Paper URL: https://www.nature.com/articles/ncomms12737
Data URL: https://www.nature.com/articles/ncomms12737#supplementary-information
Code URL: https://www.nature.com/articles/ncomms12737#supplementary-information
Useful programming skills: R, Perl
8. Evaluation of App-Embedded Disease Scales for Aiding Visual Severity Estimation of Cercospora Leaf Spot of Table Beet
Del Ponte EM, Nelson SC, Pethybridge SJ (2019) Evaluation of App-Embedded Disease Scales for Aiding Visual Severity Estimation of Cercospora Leaf Spot of Table Beet. Plant disease 103:1347-1356. 10.1094/PDIS-10-18-1718-RE
submitted by Emerson M. Del Ponte
Why should we attempt to reproduce this paper?
There are data and code written in RMarkdown which allows to reproduce the entire analysis and plots shown of the paper. It also allows to generate HTML document, which is a nice interface that facilitates the reader to understand better why some procedures were adopted and how to run them.
Paper URL: https://apsjournals.apsnet.org/doi/10.1094/PDIS-10-18-1718-RE
Data URL: https://osf.io/ezxps/
Code URL: https://github.com/emdelponte/paper-estimate-app
Useful programming skills: R
9. Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers
Phys. Chem. Chem. Phys., 2019,21, 6133-6141
submitted by Andrew McCluskey
Why should we attempt to reproduce this paper?
I believe this represents the only example of a reproducible paper from scattering data collected at Diamond Light Source (UK) and the Institute Laue-Langevin (France)
Paper URL: https://doi.org/10.1039/c9cp00203k
Data URL: https://doi.org/10.15125/BATH-00548
Code URL: https://doi.org/10.5281/zenodo.2577796
Useful programming skills: Python, make
10. A multiscale Bayesian inference approach to analyzing subdiffusion in particle trajectories
K. Hinsen and G.R. Kneller, J. Chem. Phys. 145, 151101 (2016)
submitted by Konrad Hinsen
Why should we attempt to reproduce this paper?
This is one of the very few papers in biomolecular simulation for which code and data are available and which should be reproducible. But it is also three years old, so it is an interesting test case for the longevity of reproducible research. The infrastructure software is available at http://www.activepapers.org/python-edition/ (with instructions for installation and use)
Paper URL: https://doi.org/10.1063/1.4965881
Data URL: https://doi.org/10.5281/zenodo.162171
Code URL: https://doi.org/10.5281/zenodo.162171
Useful programming skills: Python
11. Resolving the Measurement Uncertainty Paradox in Ecological Management
Memarzadeh, M., & Boettiger, C. (2019). Resolving the Measurement Uncertainty Paradox in Ecological Management. The American Naturalist, 193(5). https://doi.org/10.1086/702704
submitted by Carl Boettiger
Why should we attempt to reproduce this paper?
This will probably be a non-trivial example to reproduce, owing to: (1) long-running code, (2) dependency on external data sources, (3) possibly challenging software dependencies – both trivial ones (e.g. setting up custom fonts and plot themes) and critical ones (requires an external R package wrapping a C++ algorithm, not available on CRAN and can sometimes have interesting compiler issues, like when Apple decided to break the clang compiler in 10.0). Ideally one could just run the R code given in the appendix on your local R session, but that may take a bit of effort. We’ve tried to take steps to address those issues by providing caches of slow-running parts, providing a docker container, and providing sufficient annotations, but who knows!
Paper URL: https://doi.org/10.1086/702704
Data URL: NA
Code URL: https://github.com/boettiger-lab/pomdp-intro
Useful programming skills: R
12. Comparisons of Citizen Science Data-Gathering Approaches to Evaluate Urban Butterfly Diversity
Prudic KL, Oliver JC, Brown BV, Long EC. 2018. Comparisons of Citizen Science Data-Gathering Approaches to Evaluate Urban Butterfly Diversity. Insects. 9(4):E186. doi: 10.3390/insects9040186
submitted by Kathleen Prudic
Why should we attempt to reproduce this paper?
This is a fairly digestible paper with statistical analyses and data visualization that rely heavily on open data from citizen science projects.
Paper URL: https://doi.org/10.3390/insects9040186
Data URL: https://doi.org/10.5281/zenodo.1436741
Code URL: https://doi.org/10.5281/zenodo.1436741
Useful programming skills: R
13. Bivariate spatial point patterns in the retina: a reproducible review.
Eglen SJ (2016) Bivariate spatial point patterns in the retina: a reproducible review. Journal de la Société Française de Statistique 157:33–48.
submitted by Stephen Eglen
Why should we attempt to reproduce this paper?
Tell me what I should improve!
Paper URL: https://github.com/sje30/eglen2015
Data URL: NA
Code URL: NA
Useful programming skills: R
14. A data repository and analysis framework for spontaneous neural activity recordings in developing retina
https://doi.org/10.1186/2047-217X-3-3
submitted by Stephen Eglen
Why should we attempt to reproduce this paper?
Tell me what I can improve on; maybe think of other visualisations for data?
Paper URL: https://doi.org/10.1186/2047-217X-3-3
Data URL: NA
Code URL: http://www.damtp.cam.ac.uk/user/eglen/waverepo/
Useful programming skills: R
15. Population structure and phenotypic variation of Sclerotinia sclerotiorum from dry bean (Phaseolus vulgaris) in the United States
Kamvar ZN, Amaradasa BS, Jhala R, McCoy S, Steadman JR, Everhart SE. 2017. Population structure and phenotypic variation of Sclerotinia sclerotiorum from dry bean (Phaseolus vulgaris) in the United States. PeerJ 5:e4152 https://doi.org/10.7717/peerj.4152
submitted by Zhian N. Kamvar
Why should we attempt to reproduce this paper?
This paper is reproduced weekly in a docker container on continuous integration, but it is also set up to work via local installs as well. It would be interesting to see if it’s reproducible with a human operator who knows nothing of the project or toolchain.
Paper URL: https://peerj.com/articles/4152/
Data URL: https://osf.io/k8wtm
Code URL: https://github.com/everhartlab/sclerotinia-366
Useful programming skills: R, Make and knowledge of Docker containers
16. PREPRINT: Using digital epidemiology methods to monitor influenza-like illness in the Netherlands in real-time: the 2017-2018 season
Schneider P, Van Gool C, Spreeuwenberg P, Hooiveld M, Donker GA, Barnett DJ, Paget J. Using digital epidemiology methods to monitor influenza-like illness in the Netherlands in real-time: the 2017-2018 season. BioRxiv. 2018 Jan 1:440867.
submitted by Paul Schneider
Why should we attempt to reproduce this paper?
This preprint is an attempt to reproduce Google Flu Trend in the Netherlands.
The whole paper + code is meant to be easily reproducible and transferable to other countries and/or areas. If you are familiar with time series data, lasso regression and cross validation, the analysis should be straight forward.
If anyone is interested, I could also provide influenza data for other European countries.
Paper URL: https://www.biorxiv.org/content/10.1101/440867v1.full
Data URL: https://zenodo.org/record/1459862#.XQbNIG8vNPM
Code URL: https://zenodo.org/record/1459862#.XQbNIG8vNPM
Useful programming skills: R
17. Growth Dynamics of Independent Gametophytes of Pleurosoriopsis makinoi ( Polypodiaceae)
Bulletin of the National Museum of Nature and Science, Series B (Botany) 45: 77–86
submitted by Joel Nitta
Why should we attempt to reproduce this paper?
It uses the drake R package that should make reproducibility of R projects much easier (just run make.R and you’re done). However, it does depend on very specific package versions, which are provided by the accompanying docker image.
Paper URL: https://www.joelnitta.com/publication/2019-03-27_pleurosoriopsis/
Data URL: https://github.com/joelnitta/pleurosoriopsis
Code URL: https://github.com/joelnitta/pleurosoriopsis
Useful programming skills: R
18. Open Trade Statistics
@misc{open_trade_statistics_2019, title = {OTS BETA DASHBOARD}, url = {https://shiny.tradestatistics.io/}, author = {{Open Trade Statistics}}, publisher = {Open Trade Statistics}, year = {2019}, month = {Apr}, note = {Accessed: June 22, 2019} }
submitted by Mauricio Vargas
Why should we attempt to reproduce this paper?
The focus of the project is reproducibility. Here we show the differences to access data compared to similar initiatives: https://ropensci.org/blog/2019/05/09/tradestatistics/. Also, similar projects have obscure parts, while our exposes the code from raw data downloading to dashboard creation.
Paper URL: https://shiny.tradestatistics.io
Data URL: https://api.tradestatistics.io
Code URL: https://github.com/tradestatistics
Useful programming skills: R, Shiny
19. model4you: An R Package for Personalised Treatment Effect Estimation
Seibold, H., Zeileis, A. and Hothorn, T., 2019. model4you: An R Package for Personalised Treatment Effect Estimation. Journal of Open Research Software, 7(1), p.17. DOI: http://doi.org/10.5334/jors.219
submitted by Heidi Seibold
Why should we attempt to reproduce this paper?
I guess it could be a cool learning experience. The paper is written with knitr, uses a seed, is part of the R package it describes, was openly written using version control (SVN, R-Forge) and is available in an open access journal (@up_jors).
Paper URL: http://doi.org/10.5334/jors.219
Data URL: NA
Code URL: https://r-forge.r-project.org/scm/viewvc.php/pkg/model4you/inst/JORS/?root=partykit
Useful programming skills: R, knitr (LaTeX), version control (SVN)
20. Comparing theory-driven and data-driven attractiveness models using images of real women’s faces
Holzleitner et al. (In press). Comparing theory-driven and data-driven attractiveness models using images of real women’s faces, JEP:HPP.
submitted by Ben Jones
Why should we attempt to reproduce this paper?
Complex analyses over multiple variables. In press, so we can still fix errors ahead of publication!!
Paper URL: https://psyarxiv.com/vhc5k
Data URL: https://osf.io/jurcq/
Code URL: https://osf.io/jurcq/
Useful programming skills: R
Program 📅
10:00 Coffee and Tea ☕
10:30 Welcome
10:40 Presentation about tools for Reproducible Research by Dr. Anna Krystalli
11:30 Forming groups and start hackathon 👨🏻💻 👨🏼💻
12:30 Lunch buffet 🥗 🥙
14:30 2nd presentation (tbc)
15:00 Continue hacking 👨🏽💻👩🏼💻
16:30 Drinks and bites 🍻
2. Social-evaluative threat: Stress response stages and influences of biological sex and neuroticism
Poppelaars, E. S., Klackl, J., Pletzer, B., Wilhelm, F. H., & Jonas, E. (2019b). Social-evaluative threat: Stress response stages and influences of biological sex and neuroticism. Psychoneuroendocrinology, 109, 104378. https://doi.org/10.1016/j.psyneuen.2019.104378
submitted by Eefje S. Poppelaars
Why should we attempt to reproduce this paper?
This is a small dataset with a lot of missing data, so it’s quite challenging to produce reliable results. It uses multiple imputation to fill the missing data, so it would be interesting to see whether the results hold up when this is redone. However, since the multiple imputation takes a couple of hours to run (on a decent laptop), the final multiply imputed data is also included. Additionally, multiply imputed data needs a different statistical analysis approach, which you can get familiar with.
Paper URL: https://doi.org/10.1016/j.psyneuen.2019.104378
Data URL: https://doi.org/10.1016/j.dib.2019.104645
Code URL: http://dx.doi.org/10.17632/7vj8r76s6f.10
Useful programming skills: R